Dynamic Latent Routing jointly learns discrete latent codes, routing policies, and model parameters via dynamic search to match or exceed supervised fine-tuning by 6.6 points on average in low-data settings across four datasets and six models.
Machado, André Barreto, Doina Precup, and Michael Bowling
2 Pith papers cite this work. Polarity classification is still indexing.
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Frames online zero-shot transfer with BFMs as a bandit problem and derives an eigenvalue-minimization exploration strategy under linear reward approximation.
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Dynamic Latent Routing
Dynamic Latent Routing jointly learns discrete latent codes, routing policies, and model parameters via dynamic search to match or exceed supervised fine-tuning by 6.6 points on average in low-data settings across four datasets and six models.
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Exploration and Online Transfer with Behavioral Foundation Models
Frames online zero-shot transfer with BFMs as a bandit problem and derives an eigenvalue-minimization exploration strategy under linear reward approximation.